{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:41:34Z","timestamp":1792687294100,"version":"4.5.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The clean-energy transition is a mineral-intensive undertaking, yet the financial-market literature continues to frame transition risk primarily through an oil-versus-green lens, leaving the metals that physically enable decarbonization at the analytical periphery. This paper places energy-transition minerals at the center of the system and maps the dynamic risk architecture connecting nickel and the equities of lithium, cobalt and battery-material producers, clean-energy indices, green bonds and fossil-energy equities. Using a daily panel of nine assets spanning October 2019 to June 2026 (1675 return observations), we deploy a four-part design: a time-varying-parameter vector autoregression (TVP-VAR) connectedness framework; a quantile-connectedness extension that recovers state dependence in the tails; a DCC-GARCH system that yields dynamic hedge ratios and hedging effectiveness; and a Random-Forest\/SHAP layer that identifies the economic drivers of systemic spillovers. Four results stand out. First, the complex is strongly and counter-cyclically integrated, with an average total connectedness of 61.95% that peaks near 87.78% during the COVID-19 crash and the 2022 nickel-squeeze\/Ukraine window. Second, clean-energy technology equities and battery-mineral miners are the dominant net transmitters of shocks, whereas green bonds are the largest net receivers\u2014an ordering that inverts the conventional oil-centric narrative. Third, transmission tightens in the lower (crisis) tail, where cobalt and battery-mineral miners become disproportionately strong transmitters. Fourth, clean-energy exposure is effectively hedged with lithium and, secondarily, oil-and-gas positions, while green bonds are hedging-orthogonal and function as a genuine diversifier. SHAP attribution shows that aggregate risk sentiment (VIX) and commodity-market volatility are the principal amplifiers of connectedness. The findings reorient transition-risk analysis toward a minerals-and-technology core and carry concrete implications for green-portfolio construction, critical-mineral price-stabilization policy, and financial-stability surveillance.<\/jats:p>","DOI":"10.2139\/ssrn.7656791","type":"posted-content","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:13:34Z","timestamp":1792685614000},"source":"Crossref","is-referenced-by-count":0,"title":["Critical Minerals at the Core of the Green Transition: Quantile Connectedness, Hedging, and a Machine-Learning Anatomy of Systemic Risk"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2304-528X","authenticated-orcid":true,"given":"Muhammad","family":"Adeel Abbas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-6789","authenticated-orcid":true,"given":"W. N. 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